Title :

Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants

Author(s) :

Gobert, Maxime [Auteur]
Université de Mons / University of Mons [UMONS]
Gmys, Jan [Auteur]
Université de Mons / University of Mons [UMONS]
Toubeau, Jean-François [Auteur]
Université de Mons / University of Mons [UMONS]
Vallee, Francois [Auteur]
Université de Mons / University of Mons [UMONS]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Tuyttens, Daniel [Auteur]
Université de Mons / University of Mons [UMONS]

Conference title :

PaCOS 2019 - International Workshop on the Synergy of Parallel Computing, Optimization and Simulation (part of HPCS 2019)

City :

Dublin

Country :

Irlande

Start date of the conference :

2019-07-15

English keyword(s) :

Efficient Global Optimization
Two-stage optimization
power market
surrogate models

HAL domain(s) :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Algorithme et structure de données [cs.DS]
Computer Science [cs]/Operations Research [math.OC]

English abstract : [en]

This paper presents a comparison between two surrogate-assisted optimization methods dealing with two-stage stochastic programming. The Efficient Global Optimization (EGO) framework is challenging a method coupling Genetic ...
Show more >This paper presents a comparison between two surrogate-assisted optimization methods dealing with two-stage stochastic programming. The Efficient Global Optimization (EGO) framework is challenging a method coupling Genetic Algorithm (GA) and offline-learnt kriging model for the lower stage optimization. The objective is to prove the good behavior of bayesian optimization (and in particular EGO) applied to a real-world two-stage problem with strong dependencies between the stages. The problem consists in determining the optimal strategy of an electricity market player participating in reserve (first stage) as well as day-ahead energy and real-time markets (second stage). The decisions optimized at the first stage induce constraints on the second stage so that both stages can not be dissociated. One additional difficulty is the stochastic aspect due to uncertainties of several parameters (e.g. renewable energy-based generation) that requires more computational power to be handled. Surrogate models are introduced to deal with that additional computational burden. Experiments show that the EGO-based approach gives better results than GA with offline kriging model using smaller budget.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL